Eleanor Burstin
Research Mentor(s): Chandan Kumar-Sinha
Mentor Department: Pathology
Authors: Chandan Kumar-Sinha, Somnath Mahapatra, Rahul Mannan
Session: Session 2 (10:00am – 10:50am)
Presentation Type: Poster 21
Abstract
Chromosomal instability and aneuploidy represent critical drivers in cancer progression, with prostate cancer exhibiting unique patterns of chromosomal alterations that significantly influence disease outcomes. GNPNAT1(Glucosamine-phosphate N-acetyltransferase 1) is a crucial enzyme that catalyzes a specific biochemical reaction in the hexosamine biosynthesis pathway (HBP) and has been found to influence chromosome segregation and cell cycle regulation. Recent literature suggests that it could be a crucial regulator of genomic instability. Considering the potential role of GNPNAT1 in genomic instability, we analyzed a pilot cohort of prostate cancer tissues for the immunohistochemical (IHC) expression of this novel biomarker. The methodology integrated both automated and manual assessment techniques of H&E-stained sections of prostate cancer tissues and GNPNAT1 IHC of the corresponding sections. Our research utilized the EVOS M7000 imaging system for high-resolution scanning of GNPNAT1 stained slides while employing Qupath software for quantitative analysis. This two-part assessment allowed us to manually examine nuclear features of genomic instability using H&E staining. Simultaneously, we measured the expression of GNPNAT1 within the same, specifically annotated regions of tissue samples. The integration of Qupath software with manual annotations provided a robust validation system for quantifying percent positivity, establishing a reliable methodology for characterizing tumor heterogeneity and genomic instability based on the above markers. While working on this project, I have been supervised on several scientific techniques such as H&E staining and morphological assessment of tissues, immunohistochemistry, and its principles, brightfield and fluorescence imaging techniques, and Qupath-based image analysis techniques.
